DocumentModelAdministrationClient Class
DocumentModelAdministrationClient is the Form Recognizer interface to use for building and managing models.
It provides methods for building models and classifiers, as well as methods for viewing and deleting models and classifiers, viewing model and classifier operations, accessing account information, copying models to another Form Recognizer resource, and composing a new model from a collection of existing models.
Note
DocumentModelAdministrationClient should be used with API versions
2022-08-31 and up. To use API versions <=v2.1, instantiate a FormTrainingClient.
New in version 2022-08-31: The DocumentModelAdministrationClient and its client methods.
- Inheritance
-
azure.ai.formrecognizer._form_base_client.FormRecognizerClientBaseDocumentModelAdministrationClient
Constructor
DocumentModelAdministrationClient(endpoint: str, credential: AzureKeyCredential | TokenCredential, **kwargs: Any)
Parameters
Name | Description |
---|---|
endpoint
Required
|
Supported Cognitive Services endpoints (protocol and hostname, for example: https://westus2.api.cognitive.microsoft.com). |
credential
Required
|
Credentials needed for the client to connect to Azure. This is an instance of AzureKeyCredential if using an API key or a token credential from identity. |
Keyword-Only Parameters
Name | Description |
---|---|
api_version
|
The API version of the service to use for requests. It defaults to the latest service version. Setting to an older version may result in reduced feature compatibility. To use API versions <=v2.1, instantiate a FormTrainingClient. |
Examples
Creating the DocumentModelAdministrationClient with an endpoint and API key.
from azure.core.credentials import AzureKeyCredential
from azure.ai.formrecognizer import DocumentModelAdministrationClient
endpoint = os.environ["AZURE_FORM_RECOGNIZER_ENDPOINT"]
key = os.environ["AZURE_FORM_RECOGNIZER_KEY"]
document_model_admin_client = DocumentModelAdministrationClient(
endpoint, AzureKeyCredential(key)
)
Creating the DocumentModelAdministrationClient with a token credential.
"""DefaultAzureCredential will use the values from these environment
variables: AZURE_CLIENT_ID, AZURE_TENANT_ID, AZURE_CLIENT_SECRET
"""
from azure.ai.formrecognizer import DocumentModelAdministrationClient
from azure.identity import DefaultAzureCredential
endpoint = os.environ["AZURE_FORM_RECOGNIZER_ENDPOINT"]
credential = DefaultAzureCredential()
document_model_admin_client = DocumentModelAdministrationClient(
endpoint, credential
)
Methods
begin_build_document_classifier |
Build a document classifier. For more information on how to build and train a custom classifier model, see https://aka.ms/azsdk/formrecognizer/buildclassifiermodel. New in version 2023-07-31: The begin_build_document_classifier client method. |
begin_build_document_model |
Build a custom document model. The request must include a blob_container_url keyword parameter that is an externally accessible Azure storage blob container URI (preferably a Shared Access Signature URI). Note that a container URI (without SAS) is accepted only when the container is public or has a managed identity configured, see more about configuring managed identities to work with Form Recognizer here: https://docs.microsoft.com/azure/applied-ai-services/form-recognizer/managed-identities. Models are built using documents that are of the following content type - 'application/pdf', 'image/jpeg', 'image/png', 'image/tiff', 'image/bmp', or 'image/heif'. Other types of content in the container is ignored. New in version 2023-07-31: The file_list keyword argument. |
begin_compose_document_model |
Creates a composed document model from a collection of existing models. A composed model allows multiple models to be called with a single model ID. When a document is submitted to be analyzed with a composed model ID, a classification step is first performed to route it to the correct custom model. |
begin_copy_document_model_to |
Copy a document model stored in this resource (the source) to the user specified target Form Recognizer resource. This should be called with the source Form Recognizer resource (with the model that is intended to be copied). The target parameter should be supplied from the target resource's output from calling the get_copy_authorization method. |
close |
Close the DocumentModelAdministrationClient session. |
delete_document_classifier |
Delete a document classifier. New in version 2023-07-31: The delete_document_classifier client method. |
delete_document_model |
Delete a custom document model. |
get_copy_authorization |
Generate authorization for copying a custom model into the target Form Recognizer resource. This should be called by the target resource (where the model will be copied to) and the output can be passed as the target parameter into begin_copy_document_model_to. |
get_document_analysis_client |
Get an instance of a DocumentAnalysisClient from DocumentModelAdministrationClient. |
get_document_classifier |
Get a document classifier by its ID. New in version 2023-07-31: The get_document_classifier client method. |
get_document_model |
Get a document model by its ID. |
get_operation |
Get an operation by its ID. Get an operation associated with the Form Recognizer resource. Note that operation information only persists for 24 hours. If the document model operation was successful, the model can be accessed using the get_document_model or list_document_models APIs. |
get_resource_details |
Get information about the models under the Form Recognizer resource. |
list_document_classifiers |
List information for each document classifier, including its classifier ID, description, and when it was created. New in version 2023-07-31: The list_document_classifiers client method. |
list_document_models |
List information for each model, including its model ID, description, and when it was created. |
list_operations |
List information for each operation. Lists all operations associated with the Form Recognizer resource. Note that operation information only persists for 24 hours. If a document model operation was successful, the document model can be accessed using the get_document_model or list_document_models APIs. |
send_request |
Runs a network request using the client's existing pipeline. The request URL can be relative to the base URL. The service API version used for the request is the same as the client's unless otherwise specified. Overriding the client's configured API version in relative URL is supported on client with API version 2022-08-31 and later. Overriding in absolute URL supported on client with any API version. This method does not raise if the response is an error; to raise an exception, call raise_for_status() on the returned response object. For more information about how to send custom requests with this method, see https://aka.ms/azsdk/dpcodegen/python/send_request. |
begin_build_document_classifier
Build a document classifier. For more information on how to build and train a custom classifier model, see https://aka.ms/azsdk/formrecognizer/buildclassifiermodel.
New in version 2023-07-31: The begin_build_document_classifier client method.
begin_build_document_classifier(doc_types: Mapping[str, ClassifierDocumentTypeDetails], *, classifier_id: str | None = None, description: str | None = None, **kwargs: Any) -> DocumentModelAdministrationLROPoller[DocumentClassifierDetails]
Parameters
Name | Description |
---|---|
doc_types
Required
|
Mapping of document types to classify against. |
Keyword-Only Parameters
Name | Description |
---|---|
classifier_id
|
Unique document classifier name. If not specified, a classifier ID will be created for you. |
description
|
Document classifier description. |
Returns
Type | Description |
---|---|
An instance of an DocumentModelAdministrationLROPoller. Call result() on the poller object to return a DocumentClassifierDetails. |
Exceptions
Type | Description |
---|---|
Examples
Build a document classifier.
import os
from azure.ai.formrecognizer import (
DocumentModelAdministrationClient,
ClassifierDocumentTypeDetails,
BlobSource,
BlobFileListSource,
)
from azure.core.credentials import AzureKeyCredential
endpoint = os.environ["AZURE_FORM_RECOGNIZER_ENDPOINT"]
key = os.environ["AZURE_FORM_RECOGNIZER_KEY"]
container_sas_url = os.environ["CLASSIFIER_CONTAINER_SAS_URL"]
document_model_admin_client = DocumentModelAdministrationClient(
endpoint=endpoint, credential=AzureKeyCredential(key)
)
poller = document_model_admin_client.begin_build_document_classifier(
doc_types={
"IRS-1040-A": ClassifierDocumentTypeDetails(
source=BlobSource(
container_url=container_sas_url, prefix="IRS-1040-A/train"
)
),
"IRS-1040-D": ClassifierDocumentTypeDetails(
source=BlobFileListSource(
container_url=container_sas_url, file_list="IRS-1040-D.jsonl"
)
),
},
description="IRS document classifier",
)
result = poller.result()
print(f"Classifier ID: {result.classifier_id}")
print(f"API version used to build the classifier model: {result.api_version}")
print(f"Classifier description: {result.description}")
print(f"Document classes used for training the model:")
for doc_type, details in result.doc_types.items():
print(f"Document type: {doc_type}")
print(f"Container source: {details.source.container_url}\n")
begin_build_document_model
Build a custom document model.
The request must include a blob_container_url keyword parameter that is an externally accessible Azure storage blob container URI (preferably a Shared Access Signature URI). Note that a container URI (without SAS) is accepted only when the container is public or has a managed identity configured, see more about configuring managed identities to work with Form Recognizer here: https://docs.microsoft.com/azure/applied-ai-services/form-recognizer/managed-identities. Models are built using documents that are of the following content type - 'application/pdf', 'image/jpeg', 'image/png', 'image/tiff', 'image/bmp', or 'image/heif'. Other types of content in the container is ignored.
New in version 2023-07-31: The file_list keyword argument.
begin_build_document_model(build_mode: str | ModelBuildMode, *, blob_container_url: str, prefix: str | None = None, model_id: str | None = None, description: str | None = None, tags: Mapping[str, str] | None = None, **kwargs: Any) -> DocumentModelAdministrationLROPoller[DocumentModelDetails]
Parameters
Name | Description |
---|---|
build_mode
Required
|
The custom model build mode. Possible values include: "template", "neural". For more information about build modes, see: https://aka.ms/azsdk/formrecognizer/buildmode. |
Keyword-Only Parameters
Name | Description |
---|---|
blob_container_url
|
An Azure Storage blob container's SAS URI. A container URI (without SAS) can be used if the container is public or has a managed identity configured. For more information on setting up a training data set, see: https://aka.ms/azsdk/formrecognizer/buildtrainingset. |
model_id
|
A unique ID for your model. If not specified, a model ID will be created for you. |
description
|
An optional description to add to the model. |
prefix
|
A case-sensitive prefix string to filter documents in the blob container url path. For example, when using an Azure storage blob URI, use the prefix to restrict sub folders. prefix should end in '/' to avoid cases where filenames share the same prefix. |
file_list
|
Path to a JSONL file within the container specifying a subset of documents for training. |
tags
|
List of user defined key-value tag attributes associated with the model. |
Returns
Type | Description |
---|---|
An instance of an DocumentModelAdministrationLROPoller. Call result() on the poller object to return a DocumentModelDetails. |
Exceptions
Type | Description |
---|---|
Examples
Building a model from training files.
from azure.ai.formrecognizer import (
DocumentModelAdministrationClient,
ModelBuildMode,
)
from azure.core.credentials import AzureKeyCredential
endpoint = os.environ["AZURE_FORM_RECOGNIZER_ENDPOINT"]
key = os.environ["AZURE_FORM_RECOGNIZER_KEY"]
container_sas_url = os.environ["CONTAINER_SAS_URL"]
document_model_admin_client = DocumentModelAdministrationClient(
endpoint, AzureKeyCredential(key)
)
poller = document_model_admin_client.begin_build_document_model(
ModelBuildMode.TEMPLATE,
blob_container_url=container_sas_url,
description="my model description",
)
model = poller.result()
print(f"Model ID: {model.model_id}")
print(f"Description: {model.description}")
print(f"Model created on: {model.created_on}")
print(f"Model expires on: {model.expires_on}")
print("Doc types the model can recognize:")
for name, doc_type in model.doc_types.items():
print(
f"Doc Type: '{name}' built with '{doc_type.build_mode}' mode which has the following fields:"
)
for field_name, field in doc_type.field_schema.items():
print(
f"Field: '{field_name}' has type '{field['type']}' and confidence score "
f"{doc_type.field_confidence[field_name]}"
)
begin_compose_document_model
Creates a composed document model from a collection of existing models.
A composed model allows multiple models to be called with a single model ID. When a document is submitted to be analyzed with a composed model ID, a classification step is first performed to route it to the correct custom model.
begin_compose_document_model(component_model_ids: List[str], **kwargs: Any) -> DocumentModelAdministrationLROPoller[DocumentModelDetails]
Parameters
Name | Description |
---|---|
component_model_ids
Required
|
List of model IDs to use in the composed model. |
Keyword-Only Parameters
Name | Description |
---|---|
model_id
|
A unique ID for your composed model. If not specified, a model ID will be created for you. |
description
|
An optional description to add to the model. |
tags
|
List of user defined key-value tag attributes associated with the model. |
Returns
Type | Description |
---|---|
An instance of an DocumentModelAdministrationLROPoller. Call result() on the poller object to return a DocumentModelDetails. |
Exceptions
Type | Description |
---|---|
Examples
Creating a composed model with existing models.
from azure.core.credentials import AzureKeyCredential
from azure.ai.formrecognizer import (
DocumentModelAdministrationClient,
ModelBuildMode,
)
endpoint = os.environ["AZURE_FORM_RECOGNIZER_ENDPOINT"]
key = os.environ["AZURE_FORM_RECOGNIZER_KEY"]
po_supplies = os.environ["PURCHASE_ORDER_OFFICE_SUPPLIES_SAS_URL"]
po_equipment = os.environ["PURCHASE_ORDER_OFFICE_EQUIPMENT_SAS_URL"]
po_furniture = os.environ["PURCHASE_ORDER_OFFICE_FURNITURE_SAS_URL"]
po_cleaning_supplies = os.environ["PURCHASE_ORDER_OFFICE_CLEANING_SUPPLIES_SAS_URL"]
document_model_admin_client = DocumentModelAdministrationClient(
endpoint=endpoint, credential=AzureKeyCredential(key)
)
supplies_poller = document_model_admin_client.begin_build_document_model(
ModelBuildMode.TEMPLATE,
blob_container_url=po_supplies,
description="Purchase order-Office supplies",
)
equipment_poller = document_model_admin_client.begin_build_document_model(
ModelBuildMode.TEMPLATE,
blob_container_url=po_equipment,
description="Purchase order-Office Equipment",
)
furniture_poller = document_model_admin_client.begin_build_document_model(
ModelBuildMode.TEMPLATE,
blob_container_url=po_furniture,
description="Purchase order-Furniture",
)
cleaning_supplies_poller = document_model_admin_client.begin_build_document_model(
ModelBuildMode.TEMPLATE,
blob_container_url=po_cleaning_supplies,
description="Purchase order-Cleaning Supplies",
)
supplies_model = supplies_poller.result()
equipment_model = equipment_poller.result()
furniture_model = furniture_poller.result()
cleaning_supplies_model = cleaning_supplies_poller.result()
purchase_order_models = [
supplies_model.model_id,
equipment_model.model_id,
furniture_model.model_id,
cleaning_supplies_model.model_id,
]
poller = document_model_admin_client.begin_compose_document_model(
purchase_order_models, description="Office Supplies Composed Model"
)
model = poller.result()
print("Office Supplies Composed Model Info:")
print(f"Model ID: {model.model_id}")
print(f"Description: {model.description}")
print(f"Model created on: {model.created_on}")
print(f"Model expires on: {model.expires_on}")
print("Doc types the model can recognize:")
for name, doc_type in model.doc_types.items():
print(f"Doc Type: '{name}' which has the following fields:")
for field_name, field in doc_type.field_schema.items():
print(
f"Field: '{field_name}' has type '{field['type']}' and confidence score "
f"{doc_type.field_confidence[field_name]}"
)
begin_copy_document_model_to
Copy a document model stored in this resource (the source) to the user specified target Form Recognizer resource.
This should be called with the source Form Recognizer resource (with the model that is intended to be copied). The target parameter should be supplied from the target resource's output from calling the get_copy_authorization method.
begin_copy_document_model_to(model_id: str, target: TargetAuthorization, **kwargs: Any) -> DocumentModelAdministrationLROPoller[DocumentModelDetails]
Parameters
Name | Description |
---|---|
model_id
Required
|
Model identifier of the model to copy to target resource. |
target
Required
|
<xref:azure.ai.formrecognizer.TargetAuthorization>
The copy authorization generated from the target resource's call to get_copy_authorization. |
Keyword-Only Parameters
Name | Description |
---|---|
classifier_id
|
Unique document classifier name. If not specified, a classifier ID will be created for you. |
description
|
Document classifier description. |
Returns
Type | Description |
---|---|
An instance of a DocumentModelAdministrationLROPoller. Call result() on the poller object to return a DocumentModelDetails. |
Exceptions
Type | Description |
---|---|
Examples
Copy a model from the source resource to the target resource
from azure.core.credentials import AzureKeyCredential
from azure.ai.formrecognizer import DocumentModelAdministrationClient
source_endpoint = os.environ["AZURE_FORM_RECOGNIZER_SOURCE_ENDPOINT"]
source_key = os.environ["AZURE_FORM_RECOGNIZER_SOURCE_KEY"]
target_endpoint = os.environ["AZURE_FORM_RECOGNIZER_TARGET_ENDPOINT"]
target_key = os.environ["AZURE_FORM_RECOGNIZER_TARGET_KEY"]
source_model_id = os.getenv("AZURE_SOURCE_MODEL_ID", custom_model_id)
target_client = DocumentModelAdministrationClient(
endpoint=target_endpoint, credential=AzureKeyCredential(target_key)
)
target = target_client.get_copy_authorization(
description="model copied from other resource"
)
source_client = DocumentModelAdministrationClient(
endpoint=source_endpoint, credential=AzureKeyCredential(source_key)
)
poller = source_client.begin_copy_document_model_to(
model_id=source_model_id,
target=target, # output from target client's call to get_copy_authorization()
)
copied_over_model = poller.result()
print(f"Model ID: {copied_over_model.model_id}")
print(f"Description: {copied_over_model.description}")
print(f"Model created on: {copied_over_model.created_on}")
print(f"Model expires on: {copied_over_model.expires_on}")
print("Doc types the model can recognize:")
for name, doc_type in copied_over_model.doc_types.items():
print(f"Doc Type: '{name}' which has the following fields:")
for field_name, field in doc_type.field_schema.items():
print(
f"Field: '{field_name}' has type '{field['type']}' and confidence score "
f"{doc_type.field_confidence[field_name]}"
)
close
Close the DocumentModelAdministrationClient session.
close() -> None
Keyword-Only Parameters
Name | Description |
---|---|
classifier_id
|
Unique document classifier name. If not specified, a classifier ID will be created for you. |
description
|
Document classifier description. |
Exceptions
Type | Description |
---|---|
delete_document_classifier
Delete a document classifier.
New in version 2023-07-31: The delete_document_classifier client method.
delete_document_classifier(classifier_id: str, **kwargs: Any) -> None
Parameters
Name | Description |
---|---|
classifier_id
Required
|
Classifier identifier. |
Keyword-Only Parameters
Name | Description |
---|---|
classifier_id
|
Unique document classifier name. If not specified, a classifier ID will be created for you. |
description
|
Document classifier description. |
Returns
Type | Description |
---|---|
None |
Exceptions
Type | Description |
---|---|
Examples
Delete a classifier.
document_model_admin_client.delete_document_classifier(
classifier_id=my_classifier.classifier_id
)
try:
document_model_admin_client.get_document_classifier(
classifier_id=my_classifier.classifier_id
)
except ResourceNotFoundError:
print(f"Successfully deleted classifier with ID {my_classifier.classifier_id}")
delete_document_model
Delete a custom document model.
delete_document_model(model_id: str, **kwargs: Any) -> None
Parameters
Name | Description |
---|---|
model_id
Required
|
Model identifier. |
Keyword-Only Parameters
Name | Description |
---|---|
classifier_id
|
Unique document classifier name. If not specified, a classifier ID will be created for you. |
description
|
Document classifier description. |
Returns
Type | Description |
---|---|
None |
Exceptions
Type | Description |
---|---|
Examples
Delete a model.
document_model_admin_client.delete_document_model(model_id=my_model.model_id)
try:
document_model_admin_client.get_document_model(model_id=my_model.model_id)
except ResourceNotFoundError:
print(f"Successfully deleted model with ID {my_model.model_id}")
get_copy_authorization
Generate authorization for copying a custom model into the target Form Recognizer resource.
This should be called by the target resource (where the model will be copied to) and the output can be passed as the target parameter into begin_copy_document_model_to.
get_copy_authorization(**kwargs: Any) -> TargetAuthorization
Keyword-Only Parameters
Name | Description |
---|---|
model_id
|
A unique ID for your copied model. If not specified, a model ID will be created for you. |
description
|
An optional description to add to the model. |
tags
|
List of user defined key-value tag attributes associated with the model. |
Returns
Type | Description |
---|---|
<xref:azure.ai.formrecognizer.TargetAuthorization>
|
A dictionary with values necessary for the copy authorization. |
Exceptions
Type | Description |
---|---|
get_document_analysis_client
Get an instance of a DocumentAnalysisClient from DocumentModelAdministrationClient.
get_document_analysis_client(**kwargs: Any) -> DocumentAnalysisClient
Keyword-Only Parameters
Name | Description |
---|---|
classifier_id
|
Unique document classifier name. If not specified, a classifier ID will be created for you. |
description
|
Document classifier description. |
Returns
Type | Description |
---|---|
A DocumentAnalysisClient |
Exceptions
Type | Description |
---|---|
get_document_classifier
Get a document classifier by its ID.
New in version 2023-07-31: The get_document_classifier client method.
get_document_classifier(classifier_id: str, **kwargs: Any) -> DocumentClassifierDetails
Parameters
Name | Description |
---|---|
classifier_id
Required
|
Classifier identifier. |
Keyword-Only Parameters
Name | Description |
---|---|
classifier_id
|
Unique document classifier name. If not specified, a classifier ID will be created for you. |
description
|
Document classifier description. |
Returns
Type | Description |
---|---|
DocumentClassifierDetails |
Exceptions
Type | Description |
---|---|
Examples
Get a classifier by its ID.
my_classifier = document_model_admin_client.get_document_classifier(
classifier_id=classifier_model.classifier_id
)
print(f"\nClassifier ID: {my_classifier.classifier_id}")
print(f"Description: {my_classifier.description}")
print(f"Classifier created on: {my_classifier.created_on}")
get_document_model
Get a document model by its ID.
get_document_model(model_id: str, **kwargs: Any) -> DocumentModelDetails
Parameters
Name | Description |
---|---|
model_id
Required
|
Model identifier. |
Keyword-Only Parameters
Name | Description |
---|---|
classifier_id
|
Unique document classifier name. If not specified, a classifier ID will be created for you. |
description
|
Document classifier description. |
Returns
Type | Description |
---|---|
DocumentModelDetails |
Exceptions
Type | Description |
---|---|
Examples
Get a model by its ID.
my_model = document_model_admin_client.get_document_model(model_id=model.model_id)
print(f"\nModel ID: {my_model.model_id}")
print(f"Description: {my_model.description}")
print(f"Model created on: {my_model.created_on}")
print(f"Model expires on: {my_model.expires_on}")
get_operation
Get an operation by its ID.
Get an operation associated with the Form Recognizer resource. Note that operation information only persists for 24 hours. If the document model operation was successful, the model can be accessed using the get_document_model or list_document_models APIs.
get_operation(operation_id: str, **kwargs: Any) -> OperationDetails
Parameters
Name | Description |
---|---|
operation_id
Required
|
The operation ID. |
Keyword-Only Parameters
Name | Description |
---|---|
classifier_id
|
Unique document classifier name. If not specified, a classifier ID will be created for you. |
description
|
Document classifier description. |
Returns
Type | Description |
---|---|
OperationDetails |
Exceptions
Type | Description |
---|---|
Examples
Get a document model operation by its ID.
# Get an operation by ID
if operations:
print(f"\nGetting operation info by ID: {operations[0].operation_id}")
operation_info = document_model_admin_client.get_operation(
operations[0].operation_id
)
if operation_info.status == "succeeded":
print(f"My {operation_info.kind} operation is completed.")
result = operation_info.result
if result is not None:
if operation_info.kind == "documentClassifierBuild":
print(f"Classifier ID: {result.classifier_id}")
else:
print(f"Model ID: {result.model_id}")
elif operation_info.status == "failed":
print(f"My {operation_info.kind} operation failed.")
error = operation_info.error
if error is not None:
print(f"{error.code}: {error.message}")
else:
print(f"My operation status is {operation_info.status}")
else:
print("No operations found.")
get_resource_details
Get information about the models under the Form Recognizer resource.
get_resource_details(**kwargs: Any) -> ResourceDetails
Keyword-Only Parameters
Name | Description |
---|---|
classifier_id
|
Unique document classifier name. If not specified, a classifier ID will be created for you. |
description
|
Document classifier description. |
Returns
Type | Description |
---|---|
Summary of custom models under the resource - model count and limit. |
Exceptions
Type | Description |
---|---|
Examples
Get model counts and limits under the Form Recognizer resource.
document_model_admin_client = DocumentModelAdministrationClient(
endpoint=endpoint, credential=AzureKeyCredential(key)
)
account_details = document_model_admin_client.get_resource_details()
print(
f"Our resource has {account_details.custom_document_models.count} custom models, "
f"and we can have at most {account_details.custom_document_models.limit} custom models"
)
neural_models = account_details.neural_document_model_quota
print(
f"The quota limit for custom neural document models is {neural_models.quota} and the resource has"
f"used {neural_models.used}. The resource quota will reset on {neural_models.quota_resets_on}"
)
list_document_classifiers
List information for each document classifier, including its classifier ID, description, and when it was created.
New in version 2023-07-31: The list_document_classifiers client method.
list_document_classifiers(**kwargs: Any) -> ItemPaged[DocumentClassifierDetails]
Keyword-Only Parameters
Name | Description |
---|---|
classifier_id
|
Unique document classifier name. If not specified, a classifier ID will be created for you. |
description
|
Document classifier description. |
Returns
Type | Description |
---|---|
Pageable of DocumentClassifierDetails. |
Exceptions
Type | Description |
---|---|
Examples
List all classifiers that were built successfully under the Form Recognizer resource.
classifiers = document_model_admin_client.list_document_classifiers()
print("We have the following 'ready' models with IDs and descriptions:")
for classifier in classifiers:
print(f"{classifier.classifier_id} | {classifier.description}")
list_document_models
List information for each model, including its model ID, description, and when it was created.
list_document_models(**kwargs: Any) -> ItemPaged[DocumentModelSummary]
Keyword-Only Parameters
Name | Description |
---|---|
classifier_id
|
Unique document classifier name. If not specified, a classifier ID will be created for you. |
description
|
Document classifier description. |
Returns
Type | Description |
---|---|
Pageable of DocumentModelSummary. |
Exceptions
Type | Description |
---|---|
Examples
List all models that were built successfully under the Form Recognizer resource.
models = document_model_admin_client.list_document_models()
print("We have the following 'ready' models with IDs and descriptions:")
for model in models:
print(f"{model.model_id} | {model.description}")
list_operations
List information for each operation.
Lists all operations associated with the Form Recognizer resource. Note that operation information only persists for 24 hours. If a document model operation was successful, the document model can be accessed using the get_document_model or list_document_models APIs.
list_operations(**kwargs: Any) -> ItemPaged[OperationSummary]
Keyword-Only Parameters
Name | Description |
---|---|
classifier_id
|
Unique document classifier name. If not specified, a classifier ID will be created for you. |
description
|
Document classifier description. |
Returns
Type | Description |
---|---|
A pageable of OperationSummary. |
Exceptions
Type | Description |
---|---|
Examples
List all document model operations in the past 24 hours.
from azure.core.credentials import AzureKeyCredential
from azure.ai.formrecognizer import DocumentModelAdministrationClient
endpoint = os.environ["AZURE_FORM_RECOGNIZER_ENDPOINT"]
key = os.environ["AZURE_FORM_RECOGNIZER_KEY"]
document_model_admin_client = DocumentModelAdministrationClient(
endpoint=endpoint, credential=AzureKeyCredential(key)
)
operations = list(document_model_admin_client.list_operations())
print("The following document model operations exist under my resource:")
for operation in operations:
print(f"\nOperation ID: {operation.operation_id}")
print(f"Operation kind: {operation.kind}")
print(f"Operation status: {operation.status}")
print(f"Operation percent completed: {operation.percent_completed}")
print(f"Operation created on: {operation.created_on}")
print(f"Operation last updated on: {operation.last_updated_on}")
print(
f"Resource location of successful operation: {operation.resource_location}"
)
send_request
Runs a network request using the client's existing pipeline.
The request URL can be relative to the base URL. The service API version used for the request is the same as the client's unless otherwise specified. Overriding the client's configured API version in relative URL is supported on client with API version 2022-08-31 and later. Overriding in absolute URL supported on client with any API version. This method does not raise if the response is an error; to raise an exception, call raise_for_status() on the returned response object. For more information about how to send custom requests with this method, see https://aka.ms/azsdk/dpcodegen/python/send_request.
send_request(request: HttpRequest, *, stream: bool = False, **kwargs) -> HttpResponse
Parameters
Name | Description |
---|---|
request
Required
|
The network request you want to make. |
Keyword-Only Parameters
Name | Description |
---|---|
stream
|
Whether the response payload will be streamed. Defaults to False. |
Returns
Type | Description |
---|---|
The response of your network call. Does not do error handling on your response. |
Exceptions
Type | Description |
---|---|
Azure SDK for Python